Re: [Rhythmbox-devel] Summer of Code Proposal: A predictive playback plugin for Rhythmbox

["Derek Cramer" <cramerd gmail com>]

You might consider checking out IMMS (http://www.luminal.org/wiki/index.php/IMMS/IMMS). It is not exactly the same thing, but it does incorporate some of the same ideas, such as using signal analysis to determine similarity and choose songs based on your current listening mood. As an aside, I've always wondered why a Rhythmbox plugin never appeared for IMMS. As someone who listens to a lot of shuffled playlists, it is something I would like to see. Unfortunately I don't currently have the programming know-how to do it. :-(

On 07/04/2008, Charlotte Curtis <c f curtis gmail com> wrote:

Thanks for the reply, Alexandre,

You're definitely right in terms of relating similarity - it seems like a very subjective task, and even if signal analysis could detect similarities, it'll never be able to relate things like "we listened to those songs on our first date".  The song skipping/neural network idea is a good one too... Maybe something along the lines of keeping track of which song was skipped, looking at which similarity metric dominated that song's similarity decision, and perhaps re-weighting accordingly.  Of course, people skip songs for many reasons, so it might not be a good idea to make any permanent changes to the similarity factors.

I downloaded your last.fm plugin and while I haven't used it extensively, I'm really impressed with it.  It definitely seemed to be a more cohesive group of music than just plain "random".  However,  I think the behaviour is a little different than the plugin I'm proposing - last.fm seems to choose songs based on "people who like x tend to like y", but it does come up with things that aren't really similar musically (for example, if I start with "Ben Folds Five - Brick", the next song I get is "Weezer - Say It Ain't So").  I was hoping to make something that would allow you to say "I'm in a mellow acoustic mood", and get a random playlist of mellow acoustic songs, even if they're from a band that typically does harder songs, or if they're from a relatively unknown band (something that last.fm seems to have a hard time with - if I put on a Bonnie Prince Billy song, last.fm can't come up with any suggestions that are in my library).

It's definitely a challenging task, and it'll be interesting to see whether a music analysis method would come up with any results that mean something to discerning human ears.  If I do end up working on this project, would you mind if I included your last.fm information as a similarity option?  It does seem to be more consistent with respect to genre, at least for artists if not individual songs.  Thanks again,

Charlotte

-- 
Regards,
Derek